{
  "abstract": "Improving agricultural water-use efficiency under increasing water scarcity is a central challenge for sustainable agriculture. However, intelligent irrigation control is constrained by the high cost of trial-and-error interaction in real fields, while purely reinforcement-learning-based approaches have limited ability to explicitly represent soil water dynamics and crop physiological processes. To address these limitations, this study proposes IrriTwin, a digital-twin-oriented irrigation scheduling framework that couples the Soft Actor-Critic (SAC) algorithm with a soil water model and the GreenLab crop growth model, thereby establishing a model-in-the-loop virtual soil–crop–water environment for policy learning under water resource constraints. Experimental results show that, under the same total irrigation amount, the SAC-based strategy achieves higher fruit biomass than baseline methods. The learned policy exhibits dynamic adaptability to soil moisture conditions and crop phenological stages, effectively balancing vegetative growth and reproductive demands. Additional comparisons with reinforcement learning baselines further indicate that the proposed dual-action SAC achieves more stable convergence and higher final returns in the representative maize case. Stability analysis further shows that the strategy remains effective under simulated rainfall and irrigation-failure disturbances. Experiments on sunflower and maize demonstrate the effectiveness of IrriTwin. These results indicate that integrating reinforcement learning with mechanistic soil–crop–water models provides a promising foundation for digital-twin-enabled, precise, and water-efficient irrigation management.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "Menghua Xu"
    },
    {
      "name": "Xiujuan Wang"
    },
    {
      "name": "Mengzhen Kang"
    },
    {
      "name": "Fangle Chang"
    },
    {
      "name": "Jing Hua"
    },
    {
      "name": "Haoyu Wang"
    },
    {
      "name": "Philippe de Reffye"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/a43hn2gvnpmj0.bib",
  "cited_by": 2,
  "doi": "10.1016/j.jhydrol.2026.136034",
  "fulltext_url": null,
  "id": "a43hn2gvnpmj0",
  "issns": [
    "0022-1694",
    "1879-2707"
  ],
  "landing_url": "https://hal.inrae.fr/hal-05743603v1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.sdv"
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      "hal",
      "1.sdv.sa"
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      "hal",
      "2.sdv.sa.agro"
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    [
      "openalex",
      "1111|Irrigation Practices and Water Management"
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  "openalex": "W7169498317",
  "pdf_url": "https://hal.inrae.fr/hal-05743603/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/a43hn2gvnpmj0.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:31Z",
      "native_id": "hal-05743603",
      "source": "hal",
      "url": "https://hal.inrae.fr/hal-05743603v1"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:1111|Irrigation Practices and Water Management",
      "subfield": "hydrology-soils"
    }
  ],
  "title": "IrriTwin: Integrating reinforcement learning with soil water and crop models toward digital-twin-oriented irrigation scheduling",
  "title_text": "IrriTwin: Integrating reinforcement learning with soil water and crop models toward digital-twin-oriented irrigation scheduling",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:31Z",
  "url": "https://research.draconitic.com/paper/a43hn2gvnpmj0",
  "venue": "Journal of Hydrology",
  "versions": []
}